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   "cell_type": "markdown",
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   "source": [
    "# End of week 1 exercise\n",
    "\n",
    "To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question,  \n",
    "and responds with an explanation. This is a tool that you will be able to use yourself during the course!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1070317-3ed9-4659-abe3-828943230e03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import os\n",
    "import json\n",
    "from dotenv import load_dotenv\n",
    "from IPython.display import Markdown\n",
    "from openai import OpenAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# constants\n",
    "\n",
    "MODEL_GPT = 'gpt-4o-mini'\n",
    "MODEL_LLAMA = 'llama3.2'\n",
    "OLLAMA_BASE_URL = \"http://localhost:11434/v1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8d7923c-5f28-4c30-8556-342d7c8497c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up environment\n",
    "\n",
    "load_dotenv(override=True)\n",
    "api_key = os.getenv('OPENAI_API_KEY')\n",
    "\n",
    "if api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n",
    "    print(\"API key looks good so far\")\n",
    "else:\n",
    "    print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n",
    "    \n",
    "openai = OpenAI()\n",
    "ollama = OpenAI(base_url=OLLAMA_BASE_URL, api_key='ollama')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f0d0137-52b0-47a8-81a8-11a90a010798",
   "metadata": {},
   "outputs": [],
   "source": [
    "# here is the question; type over this to ask something new\n",
    "\n",
    "question = \"\"\"\n",
    "Please explain what this code does and why:\n",
    "yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df0d958f",
   "metadata": {},
   "outputs": [],
   "source": [
    "system_prompt = \"\"\"You are individual that possesses a unique\n",
    "and highly valuable combination of deep technical\n",
    "expertise and excellent communication skills.\n",
    "You  grasp complex, specialized concepts and then distill\n",
    "them into simple, understandable terms for people without\n",
    "the same technical background.\n",
    "\n",
    "Present your answer as markdown\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13506dd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "messages = [\n",
    "    {\"role\": \"system\", \"content\": system_prompt},\n",
    "    {\"role\": \"user\", \"content\": question}\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60ce7000-a4a5-4cce-a261-e75ef45063b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get gpt-4o-mini to answer, with streaming\n",
    "\n",
    "response = openai.chat.completions.create(model=MODEL_GPT, messages=messages)\n",
    "result = response.choices[0].message.content\n",
    "display(Markdown(result))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Llama 3.2 to answer\n",
    "\n",
    "response = ollama.chat.completions.create(model=MODEL_LLAMA, messages=messages)\n",
    "result = response.choices[0].message.content\n",
    "display(Markdown(result))"
   ]
  }
 ],
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